A Novel Hybrid Approach to Pattern Recognition of Finger Movements and Grasping Gestures in Upper Limb Amputees

2021 ◽  
pp. 1-1
Author(s):  
Saeed Bahrami Moqadam ◽  
Ahamd Saleh Asheghabadi ◽  
Jing Xu
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Hybrid Approach ◽  
Finger Movements

INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

2021 ◽  
pp. 2150043
Author(s):  
SIDHARTH PANCHOLI ◽  
AMIT M. JOSHI
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Time Derivative ◽  
Machine Learning Algorithms ◽  
Prosthetic Control ◽  
Myoelectric Prosthesis ◽  
Feature Extraction Method ◽  
Similar Work ◽  
Emg Signal

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

scholarly journals Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 10150-10165 ◽  
Author(s):  
Oluwarotimi Williams Samuel ◽  
Mojisola Grace Asogbon ◽  
Yanjuan Geng ◽  
Ali H. Al-Timemy ◽  
Sandeep Pirbhulal ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Control Method ◽  
Future Prospects ◽  
Emg Pattern Recognition

scholarly journals Pattern Recognition and Characterization of Upper Limb Neuromuscular Dynamics during Driver-Vehicle Interactions

iScience ◽  
2020 ◽  
Vol 23 (9) ◽  
pp. 101541
Author(s):  
Yang Xing ◽  
Chen Lv ◽  
Yifan Zhao ◽  
Yahui Liu ◽  
Dongpu Cao ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Neuromuscular Dynamics

Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control

2014 ◽  
Vol 61 (4) ◽  
pp. 1167-1176 ◽  
Author(s):  
Sebastian Amsuss ◽  
Peter M. Goebel ◽  
Ning Jiang ◽  
Bernhard Graimann ◽  
Liliana Paredes ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Recognition System ◽  
Surface Emg ◽  
Pattern Recognition System ◽  
Upper Limb Prosthesis ◽  
Prosthesis Control ◽  
Limb Prosthesis

scholarly journals Machine Learning Techniques and Syntactic Pattern Recognition Based Heart Disease Prediction for Smart Health

2021 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Heart Disease ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Syntactic Pattern Recognition ◽  
Minimal Processing ◽  
Learning Stage ◽  
Syntactic Pattern ◽  
Pattern Recognition Approach

Cardiovascular disease (CVD) may sometimes unexpected loss of life. It affects the heart and blood vessels of body. CVD plays an important factor of life since it may cause death of human. It is necessary to detect early of this disease for securing patients life. In this chpter two exclusively different methods are proposed for detection of heart disease. The first one is Pattern Recognition Approach with grammatical concept and the second one is machine learning approach. In the syntactic pattern recognition approach initially ECG wave from different leads is decomposed into pattern primitive based on diagnostic criteria. These primitives are then used as terminals of the proposed grammar. Pattern primitives are then input to the grammar. The parsing table is created in a tabular form. It finally indicates the patient with any disease or normal. Here five diseases beside normal are considered. Different Machine Learning (ML) approaches may be used for detecting patients with CVD and assisting health care systems also. These are useful for learning and utilizing the patterns discovered from large databases. It applies to a set of information in order to recognize underlying relationship patterns from the information set. It is basically a learning stage. Unknown incoming set of patterns can be tested using these methods. Due to its self-adaptive structure Deep Learning (DL) can process information with minimal processing time. DL exemplifies the use of neural network. A predictive model follows DL techniques for analyzing and assessing patients with heart disease. A hybrid approach based on Convolutional Layer and Gated-Recurrent Unit (GRU) are used in the paper for diagnosing the heart disease.

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